Recursive Block Coding--A New Approach to Transform Coding

Farrelle, Paul Michael; Jain, Anil K.

doi:10.1109/tcom.1986.1096509

Cited by 46 publications

(12 citation statements)

References 18 publications

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“…Such mapping involves a M -point DCT-VI and an M − 1-point DST-VI. By inverting of both sides in (2) and (3) we can obtain similar relationships between DFT and pairs of DCT/DST transforms of types III and VII. This follows from (1).…”

Section: Proof Wang and Huntmentioning

confidence: 92%

“…[22][23][24][25][26] On the other hand, such algorithms constructed using mappings (2) and (3) may not be least complex possible. We note, for example, that besides DFT, computation according to (2) or (3) also involves M − 1 planar rotation operations (given by matrix U ), which adds considerable overhead.…”

Section: Proof Wang and Huntmentioning

confidence: 99%

“…1 The DST of type-I was suggested as a basis for Recursive Block Coding technique. 2 The DCT and DST of type IV are used in design of Lapped Orthogonal Transforms, 3 serving as core filterbanks in speech and audio coding algorithms, such as G.722.1, G.718, MPEG-4 AAC, and others. 4 The DCT and DST transforms of types I, II, III, and IV, form a group of so-called "even" sinusoidal transforms.…”

Section: Introductionmentioning

confidence: 99%

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Fast computing of discrete cosine and sine transforms of types VI and VII

Chivukula

Reznik

2011

SPIE Proceedings

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Section: Proof Wang and Huntmentioning

confidence: 92%

Section: Proof Wang and Huntmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast computing of discrete cosine and sine transforms of types VI and VII

Chivukula

Reznik

2011

SPIE Proceedings

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“…These algorithms can be generally grouped into two major categories: One is to use different encoding schemes (Farrelle and Jain, 1986;Lynch et al, 1994;Malvar and Staelin, 1989;Pearson and Whybray, 1984), and the other is to postprocess the reconstructed images. Because the postprocessing techniques do not require changes to the existing standards, they are the more practical solutions.…”

Section: Introductionmentioning

confidence: 99%

Real‐time video postprocessing for deblocking and deringing on mediaprocessors

Gao

Mermer

Kim

2003

Int J Imaging Syst Tech

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Blocking and ringing are two major artifacts in highly compressed images and videos coded by block-based discrete cosine transform. Many existing deblocking and deringing algorithms are computationally very expensive and/or cannot produce satisfactory results at very low bit rates. We have developed an adaptive deblocking algorithm and a clustering-based deringing algorithm. These algorithms can smooth out the blocking and ringing artifacts while preserving the strong edges and texture areas. In addition, our postprocessing algorithms have low computational cost. We have implemented them on a commercially available, programmable processor. For an image size of 352 ϫ 288, our algorithms take only 3.17 ms, demonstrating the feasibility of real-time video postprocessing.

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“…These algorithms can be generally grouped into two major categories. One is to use different encoding schemes, such as the interleaved block transform [5], [6], the lapped transform [7], [8], and the combined transform [9]. The other is to postprocess the reconstructed images [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

A de-blocking algorithm and a blockiness metric for highly compressed images

Gao

Mermer

Kim

2002

IEEE Trans. Circuits Syst. Video Technol.

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Blockiness is a typical artifact in reconstructed images that have been coded by a block-based discrete cosine transform (BDCT). In highly compressed images and video, the blocking artifacts are easily noticeable as the discontinuities between relatively homogeneous regions. Many current noniterative de-blocking algorithms attempt to remove the blocking artifacts by smoothing a few pixels around the block boundaries; however, the results are not satisfactory, especially at very low bit rates. We propose a de-blocking algorithm based on the number of connected blocks in a relatively homogeneous region, the magnitude of abrupt changes between neighboring blocks, and the quantization step size of DCT coefficients. Due to its adaptability, the proposed algorithm can smooth out the blocking artifacts while keeping the strong edges and texture areas untouched. Since this algorithm is noniterative and only identifies those block pairs that actually need de-blocking, its computation cost is low. In addition, we have developed a new metric to measure the blocking artifacts in images. Through analyzing the 2 -point ( is the block size) one-dimensional DCT coefficients of the two neighboring blocks with blocking artifacts, we show that all of the even DCT coefficients of the combined 2 points are zeros (except frequency = 0). The odd DCT coefficients are proportional to the pixel value difference between these two blocks with their magnitudes almost inversely proportional to frequency . We selected the first DCT coefficient (frequency = 1) as an indicator for the strength of blocking artifacts in the reconstructed images. For the postprocessed images, we used a weighted summation of the squared first DCT coefficient to measure their blocking artifacts. Experimental results demonstrate that our proposed de-blocking algorithm produces better results than other methods, both visually and quantitatively, while the proposed blocking artifact metric is more consistent with subjective evaluation than the peak signal-to-noise ratio.Index Terms-Adaptive de-blocking, blocking artifact metric, discrete cosine transform, quantization parameter, video postprocessing.

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Recursive Block Coding--A New Approach to Transform Coding

Cited by 46 publications

References 18 publications

Fast computing of discrete cosine and sine transforms of types VI and VII

Fast computing of discrete cosine and sine transforms of types VI and VII

Real‐time video postprocessing for deblocking and deringing on mediaprocessors

A de-blocking algorithm and a blockiness metric for highly compressed images

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